Cognitive benefit measure related to hearing-assistance device use

ABSTRACT

A computing system comprising one or more electronic computing devices receives data from a hearing-assistance device. The computing system determines, based on the data received from the hearing-assistance device, a cognitive benefit measure for a wearer of the hearing-assistance device. The cognitive benefit measure being an indication of a change of a cognitive benefit of the wearer of the hearing-assistance device attributable to use of the hearing-assistance device by the wearer of the hearing-assistance device. The computing device outputs an indication of the cognitive benefit measure.

This application is a continuation of U.S. patent application Ser. No.16/884,793, filed May 27, 2020, which is a continuation of U.S. patentapplication Ser. No. 16/110,996, issued as U.S. Pat. No. 10,674,285,filed Aug. 23, 2018, which claims the benefit of U.S. Provisional PatentApplication 62/550,353, filed Aug. 25, 2017, the entire content of eachof which is incorporated by reference.

TECHNICAL FIELD

This disclosure relates to hearing-assistance devices.

BACKGROUND

In many people, hearing loss is a gradual process that occurs over manyyears. As a result, many people grow accustomed to living with reducedhearing without recognizing the auditory experiences and opportunitiesthey are missing. For example, a person might not realize how much lessconversation he or she engages in due to his or her hearing loss. As aresult of hearing loss, reduced audibility, and reduced socialinteraction, patients also experience follow-on effects such asdementia, depression, and generally poorer health.

SUMMARY

This disclosure describes techniques for improvement ofhearing-assistance device use. As described herein, a computing systemreceives data from a hearing-assistance device. Based on the receiveddata, the computing system determines a cognitive benefit measure for awearer of the hearing-assistance device. The cognitive benefit measuremay be an indication of a change of a cognitive benefit of the wearer ofthe hearing-assistance device attributable to use of thehearing-assistance device by the wearer of the hearing-assistancedevice. Having knowledge of the cognitive benefit measure may providethe wearer of the hearing-assistance device with a way to quantify thevalue of use of the hearing-assistance device in a way that may not bepossible without the use of data from the hearing-assistance deviceitself. In some examples, a body fitness measure (e.g., a body score)may also be determined, e.g., based on data from a hearing-assistancedevice. The body fitness measure for the wearer of thehearing-assistance device may be a measure of physical activity in whichthe wearer of the hearing-assistance device engages while wearing thehearing-assistance device. In some examples, a wellness measure (e.g., awellness score) may also be determined. The wellness measure may, forexample, be determined based on the body fitness measure and thewellness measure.

In one example, this disclosure describes a method comprising:receiving, by a computing system comprising one or more electroniccomputing devices, data from a hearing-assistance device; determining,by a computing system, based on the data received from thehearing-assistance device, a cognitive benefit measure for a wearer ofthe hearing-assistance device, the cognitive benefit measure being anindication of a change of a cognitive benefit of the wearer of thehearing-assistance device attributable to use of the hearing-assistancedevice by the wearer of the hearing-assistance device; and outputting,by the computing system, an indication of the cognitive benefit measure.

In another example, this disclosure describes a computing systemcomprising: a radio configured to receive data from a hearing-assistancedevice; and one or more processors configured to: determine, based onthe data received from the hearing-assistance device, a cognitivebenefit measure for a wearer of the hearing-assistance device, thecognitive benefit measure being an indication of a change of a cognitivebenefit of the wearer of the hearing-assistance device attributable touse of the hearing-assistance device by the wearer of thehearing-assistance device; and output an indication of the cognitivebenefit measure.

In another example, this disclosure describes a non-transitorycomputer-readable storage medium having instructions stored thereonthat, when executed, cause a computing system to: receive data from ahearing-assistance device; determine, based on the data received fromthe hearing-assistance device, a cognitive benefit measure for a wearerof the hearing-assistance device, the cognitive benefit measure being anindication of a change of a cognitive benefit of the wearer of thehearing-assistance device attributable to use of the hearing-assistancedevice by the wearer of the hearing-assistance device; and output anindication of the cognitive benefit measure.

The details of one or more aspects of the disclosure are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the techniques described in this disclosurewill be apparent from the description, drawings, and claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example system for providing cognitive benefitmeasurements related to use of a hearing-assistance device, inaccordance with one or more aspects of this disclosure.

FIG. 2 is a conceptual diagram illustrating contributions ofsub-components to a cognitive benefit measure, in accordance with one ormore aspects of this disclosure.

FIG. 3 is a block diagram illustrating example components of ahearing-assistance device, in accordance with one or more aspects ofthis disclosure.

FIG. 4 is a block diagram illustrating example components of a mobilecomputing device, in accordance with one or more aspects of thisdisclosure.

FIG. 5 is a flowchart illustrating an example operation of a computingsystem, in accordance with one or more aspects of this disclosure.

FIG. 6 is a flowchart illustrating an example operation to compute abody fitness measure in accordance with one or more aspects of thisdisclosure.

FIG. 7 is a flowchart illustrating an example operation to compute awellness measure in accordance with one or more aspects of thisdisclosure.

FIG. 8 is an example graphical user interface (GUI) for display of acognitive benefit measure in accordance with one or more aspects of thisdisclosure.

FIG. 9 is an example GUI for display of a body fitness measure inaccordance with one or more aspects of this disclosure.

FIG. 10 is an example GUI for display of a wellness measure inaccordance with one or more aspects of this disclosure.

DETAILED DESCRIPTION

In this disclosure, ordinal terms such as “first,” “second,” “third,”and so on, are not necessarily indicators of positions within an order,but rather may be used to distinguish different instances of the samething. Examples provided in this disclosure may be used together,separately, or in various combinations.

FIG. 1 illustrates an example system 100 for providing cognitive benefitmeasurements related to use of a hearing-assistance device, inaccordance with one or more aspects of this disclosure. In the exampleof FIG. 1, system 100 comprises a hearing-assistance device 102 and acomputing system 104. Computing system 104 comprises one or moreelectronic devices. For instance, in the example of FIG. 1, computingsystem 104 comprises a mobile device 106, a server device 108, and acommunication network 110.

Hearing-assistance device 102 may comprise one or more of various typesof devices configured to provide hearing assistance. For example,hearing-assistance device 102 may comprise a hearing aid device. Inanother example, hearing-assistance device 102 may comprise a PersonalSound Amplification Product (PSAP). In another example,hearing-assistance device 102 may comprise a hearable with amplificationfeatures. In other examples, hearing-assistance device 102 may compriseother types of devices that assist with hearing. The techniques of thisdisclosure are not limited to the form of hearing-assistance deviceshown in FIG. 1.

Hearing-assistance device 102 is configured to communicate wirelesslywith computing system 104. For example, hearing-assistance device 102and computing system 104 may communicate wirelessly using a BLUETOOTH™technology, a WIFI™ technology, or another type of wirelesscommunication technology. In the example of FIG. 1, hearing-assistancedevice 102 may communicate wirelessly with mobile device 106. In someexamples, hearing-assistance device 102 may use a 2.4 GHz frequency bandfor wireless communication with mobile device 106 or other computingdevices.

Mobile device 106 may communicate with server device 108 viacommunication network 110. Communication network 110 may comprise one ormore of various types of communication networks, such as cellular datanetworks, WIFI™ networks, the Internet, and so on. Mobile device 106 maycommunicate with server device 108 to store data to and retrieve datafrom server device 108. Thus, from the perspective of mobile device 106and hearing-assistance device 102, server device 108 may be consideredto be in the “cloud.”

Hearing-assistance device 102 may implement a variety of features thathelp a wearer of hearing-assistance device 102 hear better. For example,hearing-assistance device 102 may amplify the intensity of incomingsound, amplify the intensity of certain frequencies of the incomingsound, or translate or compress frequencies of the incoming sound. Inanother example, hearing-assistance device 102 may implement adirectional processing mode in which hearing-assistance device 102selectively amplifies sound originating from a particular direction(e.g., to the front of the wearer) while potentially fully or partiallycanceling sound originating from other directions. In other words, adirectional processing mode may selectively attenuate off-axis unwantedsounds. The directional processing mode may help wearers understandconversations occurring in crowds or other noisy environments. In someexamples, hearing-assistance device 102 may reduce noise by cancelingout or attenuating certain frequencies. Furthermore, in some examples,hearing-assistance device 102 may help a wearer enjoy audio media, suchas music or sound components of visual media, by outputting sound basedon audio data wirelessly transmitted to hearing-assistance device 102.

As previously mentioned, a person may lose their hearing gradually overthe course of many years. Because hearing loss may be a slow process, aperson who is gradually losing his or her hearing may grow accustomed toliving with impaired hearing and not realize the value added to theperson's life by being able to fully access the auditory environment.For instance, the person may not realize how much less time he or shespends in conversation or enjoying audio media because of the person'shearing loss. This may remain true even after a person acquires ahearing-assistance device. That is, because a person having ahearing-assistance device does not always wear the hearing-assistancedevice, the person may not realize the extent to which thehearing-assistance device enhances his or her life while wearing thehearing-assistance device as opposed to when the person is not wearingthe hearing-assistance device.

Research has shown that people who more frequently interact with othersand their environments tend to have better cognitive skills and betteremotional health, both of which may lead to better health outcomes.However, depression and physical inactivity may be more common amongpeople who seldom converse with others. This problem may be especiallyacute for older people, who are more likely to have hearing loss.

In accordance with one or more techniques of this disclosure, acognitive benefit measure is calculated based on data collected byhearing-assistance device 102. In some examples, the cognitive benefitmeasure is an indication of a change of a cognitive benefit of thewearer of hearing-assistance device 102 attributable to use ofhearing-assistance device 102 by the wearer of hearing-assistance device102. In some examples, hearing-assistance device 102 calculates thecognitive benefit measure. In other examples, the cognitive benefitmeasure is calculated by one or more computing devices of computingsystem 104. For instance, in the example of FIG. 1, mobile device 106 orserver device 108 may calculate the cognitive benefit measure. For easeof explanation, many of the examples of this disclosure describecomputing system 104 calculating the cognitive benefit measure. However,these examples can be adapted to scenarios where hearing-assistancedevice 102 calculates the cognitive benefit measure.

As described herein, computing system 104 may calculate a cognitivebenefit measure for a wearer of hearing-assistance device 102 based on aplurality of sub-components of the cognitive benefit measure. Forexample, as part of determining the cognitive benefit measure, computingsystem 104 may determine a plurality of sub-components of the cognitivebenefit measure and may determine the cognitive benefit measure based onthe plurality of sub-components of the cognitive benefit measure. Insome examples, hearing-assistance device 102 determines one or more ofthe sub-components of the cognitive benefit measure. In some examples,the sub-components include one or more of an “audibility” sub-component,an “intelligibility” sub-component, a “comfort” sub-component, a “focus”sub-component, a “sociability” sub-component, and a “connectivity”sub-component. In some examples, each of the sub-components shares acommon range (e.g., from 0 to 100), which may make combination of dataefficient. In some examples, computing system 104 may reset each of thesub-components for each scoring period. For instance, computing system104 may reset the values of the sub-components once per day or otherrecurrence period.

The audibility sub-component for a wearer of hearing-assistance device102 is a measure of the improvement in audibility provided to the wearerby hearing-assistance device 102. The audibility sub-component may beconsidered the amount of environmental sounds that are quieter than thewearer's unaided audiometric thresholds, but that are made audiblethrough amplification by hearing-assistance device 102, scaled to arange used by the other sub-components. In other words, the audibilitysub-component is related to hearing more quiet sounds in the wearer'senvironment. To determine the audibility sub-component, computing system104 may compare a patient's hearing thresholds to a standardizedstimulus response across frequency. For instance, in some examples, theaudibility sub-component is calculated by subtracting the percentage ofa standardized sound stimulus (e.g., a moderate-level (65 dB SPL)long-term averaged speech input) that is audible without ahearing-assistance device from the percentage of sound that is audiblewith a hearing-assistance device; both percentages are calculated bydividing the number of audible frequency channels in hearing-assistancedevice 102 by the number of total channels in the device. A channel in ahearing-assistance device is a subset of frequencies over which theprocessing of incoming sound can be different from that at otherfrequencies. For example, a hearing aid channel may have a highpasscutoff of 1480 Hz, and a lowpass cutoff of 1720 Hz. So, the “totalchannels” in a hearing aid are the number of distinct divisions offrequency. An “audible channel” is one wherein the level of the inputstimulus (in dB SPL) plus the gain applied to the stimulus (in dB)results in an overall level that is above the hearing threshold of thelistener in that frequency range. Each of the unaided thresholdscorresponds to a different frequency. A wearer of hearing-assistancedevice 102 is unable to hear the frequency corresponding to an unaidedthreshold if an intensity of a sound at the corresponding frequency isbelow the unaided threshold. In one example, audibility sub-component iscalculated as a number of frequency bands made audible byhearing-assistance device 102 divided by a total number of frequencybands handled by hearing-assistance device 102. In this example, each ofthe frequency bands may be a contiguous range within a frequencyspectrum.

The intelligibility sub-component for the wearer of hearing-assistancedevice 102 is a numerical estimate of the improvement in speechunderstanding provided to the wearer by hearing-assistance device 102.The intelligibility sub-component may be considered a measure ofunderstanding more words in conversation. In some examples, theintelligibility sub-component is a percentage improvement inintelligibility. For instance, in one such example, the intelligibilitysub-component is equal to a first value multiplied by 100, where thefirst value is equal to a third value subtracted from a second value.The second value is equal to an aided intelligibility score, and thethird value is equal to an unaided intelligibility score. Theintelligibility scores both are calculated from the SpeechIntelligibility Index (SII), which is a standardized measure ofintelligibility. Of course, other measures of intelligibility scaled tothe same range as the other sub-components may be used.

The comfort sub-component for the wearer of hearing-assistance device102 is a numerical value indicating a measure of noise reductionprovided by hearing-assistance device 102. The comfort sub-component maybe considered a measure of noise reduction in the wearer's environment.In some examples, the comfort sub-component is equal to an average or asum of noise reduction. For instance, in one such example, the comfortsub-component is equal to a first value. In this example, the firstvalue is equal to a sum of the average noise reduction (in dB) acrossmemories and environments, weighted by the time spent in each memory ina set of one or more memories and each environment in a set of one ormore environments, scaled to the standardized range used in the othersub-components. In this example, hearing-assistance device 102 comprisesdifferent memories, which have different signal processing schemestailored to specific listening situations. For example, there is a“Restaurant” memory, a “Music” memory, and so on. Each of theenvironments is an acoustic situation that hearing-assistance device 102classifies automatically. Example types of environments include a“Speech-in-Noise” environment, a “Quiet” environment, a “Machine Noise”environment, and so on.

The focus sub-component for the wearer of hearing-assistance device 102is a numerical value indicating an amount of time hearing-assistancedevice 102 has spent in a directional processing mode. The focussub-component may be considered a measure of the wearer being able tohear sounds most important to the wearer. The focus sub-component may bescaled to be in a range used by the other sub-components. For instance,in some examples, the focus sub-component is equal to a percentage oftime spent in a directional processing mode. For instance, in one suchexample, the focus sub-component is equal to a first value multiplied by100, where the first value is equal to a second value divided by a thirdvalue; the second value being equal to an amount of time spent in adirectional processing mode; the third value being equal to the totalamount of time hearing-assistance device 102 is powered on. In anomni-directional mode, hearing-assistance device 102 does notselectively amplify or attenuate sounds from particular directions.

The sociability sub-component for the wearer of hearing-assistancedevice 102 is a numerical value indicating an amount of timehearing-assistance device 102 spent in auditory environments involvingspeech. The sociability sub-component may be considered a measure oftime spent in conversation. The sociability sub-component may be scaledto be in a range used by the other sub-components. In some examples, thesociability sub-component is a percentage of time spent in socialsituations. For instance, in one such example, the sociabilitysub-component is equal to a first value multiplied by 100, where thefirst value is equal to a second value divided by a third value. In thisexample, the second value is equal to the amount of time spent in speechand speech in noise, and the third value is equal to the total amount oftime that hearing-assistance device 102 is powered on.

The connectivity sub-component for the wearer of hearing-assistancedevice 102 is a numerical value indicating an amount of timehearing-assistance device 102 spent streaming audio data from devicesthat are wirelessly connective to hearing-assistance device 102. Theconnectivity sub-component may be considered a measure of timeconnecting with media. In some examples, the connectivity sub-componentfor the wearer is a measure of the amount of time spent streaming media(or the amount of time hearing-assistance device 102 spent maintainingconnectivity for streaming media) relative to an amount of time, such asan amount of time associated with a maximum benefit attained fromstreaming media. This measure may be on a same scale (e.g., 0 to 100, 0to 50, etc.) as the other sub-components. For instance, in one suchexample, the connectivity sub-component may be equal to a first value.In this example, the first value is equal to an amount of time spentstreaming from a separate wireless device, up to a time associated withthe maximum benefit attained from streaming media, divided by the timeassociated with the maximum benefit attained from streaming media.

Computing system 104 may determine the cognitive benefit measure basedon the sub-components in various ways. For example, computing system 104may determine the cognitive benefit measure based on an average orweighted average of the sub-components. In other words, the cognitivebenefit measure may be an average of all the sub-component data,although the sub-components may be differentially weighted beforeaveraging occurs. For example, the “connectivity” sub-component may beweighted more than the other measures because the expectation is thatthe connectivity sub-component typically yields a relatively small scorebecause patients spend only a small percentage of the time streamingaudio to their hearing aids. In some examples, computing system 104determines the weights used in calculating the weighted average bynormalizing the sub-components by a maximum benefit expected orpredicted for each sub-component.

In some examples, computing system 104 scales the cognitive benefitmeasure (and the sub-components) by use time of hearing-assistancedevice 102. For example, if a user does not wear his or herhearing-assistance device on a given day, the cognitive benefit measuremay not be calculated, but the more the user wears hearing-assistancedevice 102, the larger the cognitive benefit measure. This type ofscaling may be intuitive for the user, and time spent usinghearing-assistance device 102 may be one contributing factor to thecognitive benefit measure over which the user has the most control.

In some examples, computing system 104 may store historical cognitivebenefit measures for the wearer of hearing-assistance device 102. Forexample, computing system 104 may store a cognitive benefit measure foreach day or other time period. Additionally, computing system 104 mayoutput data based on the historical cognitive benefit measures fordisplay. In this way, the wearer of hearing-assistance device 102 may beable to track the wearer's cognitive benefit measures over time. Forinstance, the wearer of hearing-assistance device 102 may be able totrack his or her progress.

As noted above, the cognitive benefit measure may be calculated based ondata collected by hearing-assistance device 102. In some examples,hearing-assistance device 102 writes data to a data log. For example,hearing-assistance device 102 may store, in memory, counter data usedfor calculation of sub-components. For instance, hearing-assistancedevice 102 may store data indicating an amount of timehearing-assistance device 102 spent streaming media, an amount of timespent in a directional processing mode, and other values.Hearing-assistance device 102 may flush these values out to the data logon a period basis and may reset the values.

Hearing-assistance device 102 may communicate data in the data log tocomputing system 104. Computing system 104 may receive, fromhearing-assistance device 102, the data from the data log. Computingsystem 104 may use the received information to determine the cognitivebenefit measure.

Hearing-assistance device 102 may write the data to the data log on aperiodic basis, e.g., once per time period. In some examples, theduration of the time period changes during the life cycle ofhearing-assistance device 102. For example, hearing-assistance device102 may write data to the data log once every 15 minutes during thefirst two years of use of hearing-assistance device 102 and once every60 minutes following the first two years of use of hearing-assistancedevice 102. Because hearing-assistance device 102 sends data in the datalog, as opposed to the live counter data, and hearing-assistance device102 updates the data log on a periodic basis, the user may be able toaccess an updated cognitive benefit measures at least as often as thesame periodic basis.

Furthermore, in some examples, in addition to determining a cognitivebenefit measure (e.g., a brain score) for the wearer ofhearing-assistance device 102, computing system 104 may use datacollected by hearing-assistance device 102 to determine a body fitnessmeasure for the wearer of hearing-assistance device 102. The bodyfitness measure for the wearer of hearing-assistance device 102 may bean indication of physical activity in which the wearer ofhearing-assistance device 102 engages while wearing hearing-assistancedevice 102. Like the cognitive benefit measure, computing system 104 maydetermine the body fitness measure based on a plurality ofsub-components. For instance, computing system 104 may determine thebody fitness measure based on a “steps” sub-component, an “activity”sub-component, and a “move” sub-component. The “steps” component mayindicate a number of steps (e.g., while walking or running) that thewearer of hearing-assistance device 102 has taken during a currentscoring period. The “activity” sub-component may be a measure ofvigorous activity in which the wearer of hearing-assistance device 102has engaged during the current scoring period. The “move” sub-componentmay be based on a number time of intervals during the current scoringperiod in which the wearer of hearing-assistance device 102 moves for agiven amount of time. The current scoring period may be an amount oftime after which computing system 104 resets the cognitive benefitmeasure and/or the body fitness measure. For instance, the currentscoring period may be one day, one week, or another time period. Thus,the cognitive benefit measure and the body fitness measure, andsub-components thereof, may be reset periodically or recurrently.

In some examples, computing system 104 may determine values of one ormore of the sub-components of the cognitive benefit measure and the bodyfitness measure using goals. For instance, in one example with respectto the “steps” sub-component of the body fitness measure, the wearer ofhearing-assistance device 103 may set a number of steps to take during ascoring period as a goal for the “steps” sub-component. In this example,computing system 104 may determine the value of the “steps” componentbased on the progress of the wearer of hearing-assistance device 102during the scoring period toward the goal for the “steps” component. Insome examples, such goals may be user-configurable. For example,computing system 104 may permit a user (e.g., the wearer ofhearing-assistance device 102, a caregiver, a health care provider, oranother person) to set the goals for particular wearers ofhearing-assistance devices or for a population of patients. For example,wearers of hearing-assistance devices may be characterized (e.g.,classified) using one or more of various techniques, such as artificialintelligence using demographic or medical information. In this example,goal(s) may be determined based upon such characterizations aboutwearers of hearing-assistance devices.

In some examples, computing system 104 may determine a “wellness”measure (e.g., a wellness score) for the wearer of hearing-assistancedevice 102. The wellness measure for the wearer of hearing-assistancedevice 102 may be an indication of an overall wellness of the wearer ofhearing-assistance device 102. Computing system 104 may determine thewellness measure based on the cognitive benefit measure and the bodyfitness measure of the wearer of hearing-assistance device 102 for ascoring period. For instance, computing system 104 may determine thewellness measure as a weighted sum of the cognitive benefit measure, thebody fitness measure, and possibly one or more other factors. In someexamples, computing system 104 may determine the wellness measure as amultiplication product of the cognitive benefit measure and the bodyfitness measure.

In some examples, hearing-assistance device 102 calculates the bodyfitness measure and/or the wellness measure. In other examples, the bodyfitness measure and/or the wellness measure is calculated by one or morecomputing devices of computing system 104. For instance, in the exampleof FIG. 1, mobile device 106 or server device 108 may calculate the bodyfitness measure and/or the wellness measure. For ease of explanation,many of the examples of this disclosure describe computing system 104calculating the body fitness measure and/or the wellness measure.However, these examples can be adapted to scenarios wherehearing-assistance device 102 calculates the body fitness measure and/orwellness measure.

Computing system 104 may be configured to generate alerts based on oneor more of a cognitive benefit measure, body fitness measure, a wellnessmeasure of a wearer of hearing-assistance device 102, or a combinationthereof. An alert may alert the wearer of hearing-assistance device 102or another person to the occurrence of a particular condition. In otherwords, computing system 104 may generate, based on the cognitive benefitmeasure, an alert to the wearer of hearing-assistance device 102 oranother person. Computing system 104 may transmit an alert to acaregiver, healthcare professional, family member, or other person orpersons. Computing system 104 may generate an alert when one or more ofvarious conditions occur. For example, computing system 104 may generatean alert if computing system 104 detects a consistent downward trend inthe wearer's body fitness measure, cognitive benefit measure, and/orwellness measure. In another example, computing system 104 may generatean alert if computing system 104 determines that the wearer's bodyfitness measure, cognitive benefit measure, and/or wellness measure arebelow one or more thresholds for a threshold amount of time (e.g., aparticular number of days). In some examples, responsive to declarationof an alert, a therapy may be changed, or additional diagnostics may beperformed, encouragement may be provided, or a communication may beinitiated. In other examples, hearing-assistance device 102 may generatethe alerts.

In some examples, hearing-assistance device 102 does not have a realtime clock that keeps track of the current time and date. Not includingsuch a real time clock in hearing-assistance device 102 may beadvantageous for various reasons. For instance, because of the extremesize constraints on hearing-assistance device 102, the batteries ofhearing-assistance device 102 may need to be very small. Maintaining areal time clock in hearing-assistance device 102 may consume asignificant amount of power from the battery that may be better used forother purposes. Hearing-assistance device 102 may produce a clock signalthat cycles at a given frequency so that hearing-assistance device 102is able to track relative time. For instance, hearing-assistance device102 may be able to count clock cycles to determine that a given amountof time (e.g., five minutes) has passed following a given clock cycle,but without a real-time clock hearing-assistance device 102 may not beequipped to relate that relative time to an actual time and date (e.g.,11:34 A.M. on Aug. 22, 2017). Moreover, maintaining a real time clockbased on this clock signal may require hearing-assistance device 102 tocontinue the clock signal even while hearing-assistance device 102 isnot in use, which may consume a significant amount of battery power.

However, several of the sub-components of the cognitive benefit measureand the body fitness measure are time-dependent. For example, the “usescore” sub-component may be based on how much time the wearer ofhearing-assistance device 102 uses the hearing-assistance device 102during a scoring period. In another example, the “engagement”sub-component may be based at least in part on how much time the wearerof hearing-assistance device 102 engages in conversation during ascoring period and how much time the wearer of hearing-assistance device102 uses hearing-assistance device 102 to stream audio media during thescoring period. Moreover, in some examples, computing system 104 mayneed to determine times associated with log data items received fromhearing-assistance device 102 to determine whether the log data itemsare associated with a current scoring period.

This disclosure describes techniques that may overcome the problemsassociated with determining sub-components of the cognitive benefitmeasure and/or the body fitness measure in the absence of a real-timeclock in hearing-assistance device 102. For example, hearing-assistancedevice 102 may maintain a data log that stores log data items, which mayinclude sub-component data. The sub-component data may include data fromwhich values of sub-components may, at least partially, be determined.For example, an inertial measurement unit (IMU) of hearing-assistancedevice 102 may periodically write data to the data log indicating thenumber of steps taken by the wearer of hearing-assistance device 102. Inaccordance with one or more techniques of this disclosure,hearing-assistance device 102 may receive timestamps from a computingdevice in computing system 104. For example, hearing-assistance device102 may receive timestamps from mobile device 106. A timestamp may be avalue that indicates a time. For instance, a timestamp may indicate anumber of seconds that have passed since a fixed real time (e.g., sinceJan. 1, 1970). When recording data (e.g., log data items) to the datalog, hearing-assistance device 102 may include the timestamp in the logdata item. For example, hearing-assistance device 102 may record a logdata item in the data log indicating that the wearer ofhearing-assistance device 102 has started using hearing-assistancedevice 102. In this example, hearing-assistance device 102 may includethe timestamp in the log data item. In this example, computing system104 may use this data recorded in the data log to determine the “usescore” sub-component.

Thus, computing system 104 may send timestamps to hearing-assistancedevice 102. Additionally, computing system 104 may receive a pluralityof log data items from hearing-assistance device 102. Each of the logdata items may include log data and one of the timestamps sent tohearing-assistance device 102 by computing system 104. Computing system104 may determine, based on the timestamps and the log data in the logdata items, at least one of the cognitive benefit measure or the bodyfitness measure. For instance, computing system 104 may use thetimestamps in the log data items to determine which log data items arefrom a current scoring period and then only use log data in the log dataitems from the current scoring period when determining values of thesub-components of the cognitive benefit measure and/or body fitnessmeasure.

Hearing-assistance device 102 may receive timestamps from computingsystem 104 in response to one or more of various events. For example,hearing-assistance device 102 may send a timestamp request to computingsystem 104 when preparing to write data to the data log. In someexamples, hearing-assistance device 102 may periodically requesttimestamps from computing system 104. In some examples, computing system104 may be configured to periodically send timestamps tohearing-assistance device 102 on an asynchronous basis. That is, in thisexample, it may not be necessary for hearing-assistance device 102 tosend a request to computing system 104 for timestamps. For instance,computing system 104 may send a timestamp to hearing-assistance device102 once every 60 seconds, 30 seconds, or other time period. In thisexample, hearing-assistance device 102 may store the timestamp(potentially overwriting a previous version of the timestamp) and theninclude a copy of the timestamp in a log data item when storing the logdata item to the data log. Because exact precision may not be necessarywhen determining values of sub-components of the cognitive benefitmeasure and the body fitness measure, including an exactly correct timein a log data item may be unnecessary. Thus, the cycle time forhearing-assistance device 102 receiving timestamps may be set slowenough that an amount of energy consumed by wireless receiving andwriting the timestamps to memory may be less than the amount of energythat would be consumed by hearing-assistance device 102 maintaining itsown real time clock, while allowing for reasonable accuracy.

FIG. 2 is a conceptual diagram illustrating contributions ofsub-components to a cognitive benefit measure 200, in accordance withone or more aspects of this disclosure. Consistent with the exampleabove, cognitive benefit measure 200 may be determined based on anaudibility sub-component 202, an intelligibility sub-component 204, afocus sub-component 206, a connectivity sub-component 208, a sociabilitysub-component 210, and a comfort sub-component 212. Thus, in the exampleof FIG. 2, six sub-components (white circles) contribute to thecomposite cognitive benefit measure (shaded circle); the relationship isdenoted by the arrows.

In some examples, computing system 104 may output a graphical userinterface (GUI) for display on a display screen. For example, mobiledevice 106 may output the GUI for display on a display screen of mobiledevice 106. In another example, server device 108 may generate datadefining a webpage comprising the GUI and send the data mobile device106 or another computing device (e.g., a personal computer) forrendering for display by a web browser application. The GUI may includecontent similar to that shown in FIG. 2. In response to receiving anindication of user input to select a circle corresponding to a givensub-component, computing system 104 may output for display, informationabout what is being measured in the given sub-component. However, insome examples, computing system 104 does not cause actual calculationsfor the sub-components to be displayed. For example, the “sociability”sub-component is a numerical value indicating a measure of the amount oftime a wearer of hearing-assistance device 102 spends in socialsituations, but the wearer is not privy to the fact that this measure iscalculated as the amount of time an automatic environmentalclassification system of hearing-assistance device 102 detects speech.

FIG. 3 is a block diagram illustrating example components ofhearing-assistance device 102, in accordance with one or more aspects ofthis disclosure. In the example of FIG. 3, hearing-assistance device 102comprises one or more storage device(s) 300, a radio 302, a receiver304, one or more processor(s) 306, a microphone 308, a set of sensors310, a battery 312, and one or more communication channels 314.Communication channels 314 provide communication between storagedevice(s) 300, radio 302, receiver 304, processor(s) 306, a microphone308, and sensors 310. Components 300, 302, 304, 306, 308, and 310 maydraw electrical power from battery 312.

In the example of FIG. 3, sensors 310 include one or more accelerometers318. Furthermore, in examples such as that of FIG. 3, an inertialmeasurement unit (IMU) 326 includes one or more of accelerometers 318.The IMU 326 may use signals generated by accelerometers 318 for variouspurposes. For example, the IMU 326 may use the signals generated byaccelerometers 318 to count the number of steps that a wearer of hearingassistance device 102 has taken.

Additionally, in the example of FIG. 3, sensors 310 also include a heartrate sensor 320 and a body temperature sensor 323. In other examples,hearing-assistance device 102 may include more, fewer, or differentcomponents. For instance, in other examples, hearing-assistance device102 does not include particular sensors shown in the example of FIG. 3.In some examples, heart rate sensor 320 comprises a visible light sensorand/or a pulse oximetry sensor.

Storage device(s) 300 may store data. Storage device(s) 300 may comprisevolatile memory and may therefore not retain stored contents if poweredoff. Examples of volatile memories may include random access memories(RAM), dynamic random access memories (DRAM), static random accessmemories (SRAM), and other forms of volatile memories known in the art.Storage device(s) 300 may further be configured for long-term storage ofinformation as non-volatile memory space and retain information afterpower on/off cycles. Examples of non-volatile memory configurations mayinclude magnetic hard discs, optical discs, floppy discs, flashmemories, or forms of electrically programmable memories (EPROM) orelectrically erasable and programmable (EEPROM) memories.

Radio 302 may enable hearing-assistance device 102 to send data to andreceive data from one or more other computing devices. For example,radio 302 may enable hearing-assistance device 102 to send data to andreceive data from mobile device 106 (FIG. 1). Radio 302 may use one ormore of various types of wireless technology to communicate. Forinstance, radio 302 may use Bluetooth, 3G, 4G, 4G LTE, ZigBee, WiFi,Near-Field Magnetic Induction (NFMI), or another communicationtechnology.

Receiver 304 comprises one or more speakers for generating audiblesound. Microphone 308 detects incoming sound and generates an electricalsignal (e.g., an analog or digital electrical signal) representing theincoming sound. Processor(s) 306 may process the signal generated bymicrophone 308 to enhance, amplify, or cancel-out particular channelswithin the incoming sound. Processor(s) 306 may then cause receiver 304to generate sound based on the processed signal. In some examples,processor(s) 306 include one or more digital signal processors (DSPs).

Processor(s) 306 may cause radio 302 to transmit one or more of varioustypes of data. For example, processor(s) 306 may cause radio 302 totransmit data to computing system 104. Furthermore, radio 302 mayreceive audio data from computing system 104 and processor(s) 306 maycause receiver 304 to output sound based on the audio data.

In some examples, hearing-assistance device 102 is a “plug-n-play” typeof device. In some examples, hearing-assistance device 102 isprogrammable to help the user manage things like wind noise.Furthermore, in some examples, hearing-assistance device 102 comprises acustom earmold or a standard receiver module at the end of a RIC cable.The additional volume in a custom earmold may allow room for componentssuch as sensors (accelerometers, heartrate monitors, temp sensors), awoofer-tweeter, (providing richer sound for music aficionados), and anacoustic valve that provides occlusion when desired. In some examples, asix conductor RIC cable is used for in hearing-assistance devices withsensors, woofer-tweeters, and/or acoustic valves.

In the example of FIG. 3, storage device(s) 300 may store counter data322 and a data log 324. Counter data 322 may include actively updateddata used for determining sub-components of a cognitive benefit measure.For example, hearing-assistance device 102 may store data indicating anamount of time hearing-assistance device 102 spent streaming media, anamount of time spent in a directional processing mode, and other values.Processor(s) 306 may update counter data 322 at a more frequent ratethan data log 324. Processor(s) may flush values from counter data 322out to data log 324 on a period basis and may reset counter data 322.Additionally, processor(s) 306 may cause radio 302 to send data in datalog 324 to computing system 104. For instance, processor(s) 306 maycause radio 302 to send data in data log 324 to computing system 104 inresponse to radio 302 receiving a request for the data from computingsystem 104.

FIG. 4 is a block diagram illustrating example components of computingdevice 400, in accordance with one or more aspects of this disclosure.FIG. 4 illustrates only one particular example of computing device 400,and many other example configurations of computing device 400 exist.Computing device 400 may be a computing device in computing system 104(FIG. 1). For instance, computing device 400 may be mobile device 106 orserver device 108.

As shown in the example of FIG. 4, computing device 400 includes one ormore processors 402, one or more communication units 404, one or moreinput devices 408, one or more output devices 410, a display screen 412,a battery 414, one or more storage devices 416, and one or morecommunication channels 418. Computing device 400 may include many othercomponents. For example, computing device 400 may include physicalbuttons, microphones, speakers, communication ports, and so on.Communication channel(s) 418 may interconnect each of components 402,404, 408, 410, 412, and 416 for inter-component communications(physically, communicatively, and/or operatively). In some examples,communication channel(s) 418 may include a system bus, a networkconnection, an inter-process communication data structure, or any othermethod for communicating data. Battery 414 may provide electrical energyto components 402, 404, 408, 410, 412 and 416.

Storage device(s) 416 may store information required for use duringoperation of computing device 400. In some examples, storage device(s)416 have the primary purpose of being a short term and not a long-termcomputer-readable storage medium. Storage device(s) 416 may be volatilememory and may therefore not retain stored contents if powered off.Storage device(s) 416 may further be configured for long-term storage ofinformation as non-volatile memory space and retain information afterpower on/off cycles. In some examples, processor(s) 402 on computingdevice 400 read and may execute instructions stored by storage device(s)416.

Computing device 400 may include one or more input device(s) 408 thatcomputing device 400 uses to receive user input. Examples of user inputinclude tactile, audio, and video user input. Input device(s) 408 mayinclude presence-sensitive screens, touch-sensitive screens, mice,keyboards, voice responsive systems, microphones or other types ofdevices for detecting input from a human or machine.

Communication unit(s) 404 may enable computing device 400 to send datato and receive data from one or more other computing devices (e.g., viaa communications network, such as a local area network or the Internet).In some examples, communication unit(s) 404 may include wirelesstransmitters and receivers that enable computing device 400 tocommunicate wirelessly with the other computing devices. For instance,in the example of FIG. 4, communication unit(s) 404 include a radio 406that enables computing device 400 to communicate wirelessly with othercomputing devices, such as hearing-assistance device 102 (FIG. 1, FIG.3). Examples of communication unit(s) 404 may include network interfacecards, Ethernet cards, optical transceivers, radio frequencytransceivers, or other types of devices that are able to send andreceive information. Other examples of such communication units mayinclude Bluetooth, 3G, and WiFi radios, Universal Serial Bus (USB)interfaces, etc. Computing device 400 may use communication unit(s) 404to communicate with one or more hearing-assistance devices (e.g.,hearing-assistance device 102 (FIG. 1, FIG. 3)). Additionally, computingdevice 400 may use communication unit(s) 404 to communicate with one ormore other remote devices (e.g., server device 108 (FIG. 1)).

Output device(s) 410 may generate output. Examples of output includetactile, audio, and video output. Output device(s) 410 may includepresence-sensitive screens, sound cards, video graphics adapter cards,speakers, liquid crystal displays (LCD), or other types of devices forgenerating output.

Processor(s) 402 may read instructions from storage device(s) 416 andmay execute instructions stored by storage device(s) 416. Execution ofthe instructions by processor(s) 402 may configure or cause computingdevice 400 to provide at least some of the functionality ascribed inthis disclosure to computing device 400. As shown in the example of FIG.4, storage device(s) 416 include computer-readable instructionsassociated with operating system 420, application modules 422A-422N(collectively, “application modules 422”), and a companion application424. Additionally, in the example of FIG. 4, storage device(s) 416 maystore historical data 426.

Execution of instructions associated with operating system 420 may causecomputing device 400 to perform various functions to manage hardwareresources of computing device 400 and to provide various common servicesfor other computer programs. Execution of instructions associated withapplication modules 422 may cause computing device 400 to provide one ormore of various applications (e.g., “apps,” operating systemapplications, etc.). Application modules 422 may provide particularapplications, such as text messaging (e.g., SMS) applications, instantmessaging applications, email applications, social media applications,text composition applications, and so on.

Execution of instructions associated with companion application 424 maycause computing device 400 to perform one or more of various functionsdescribed in this disclosure with respect to computing system 104 (FIG.1). For example, execution of instructions associated with companionapplication 424 may cause computing device 400 to configure radio 406 towirelessly receive data from hearing-assistance device 102 (FIG. 1; FIG.3). Additionally, execution of instructions of companion application 424may cause computing device 400 to determine a cognitive benefit measure,a body fitness measure, and/or a wellness measure for a wearer ofhearing-assistance device 102 and output an indication of the cognitivebenefit measure, the body fitness measure, and/or the wellness measure.Although not explicitly recited here for the sake of brevity,instructions associated with companion application 424 may causecomputing device 400 to perform one or more of various other actions ofcomputing system 104. In some examples, companion application 424 is aninstance of a web application or server application. In some examples,such as examples where computing device 400 is a mobile device,companion application 424 may be a native application.

In some examples, a GUI of companion application 424 has a plurality ofdifferent sections, that may or may not appear concurrently. Forexample, the GUI of companion application 424 may include a section forcontrolling the intensity of sound generated by (e.g., the volume of)hearing-assistance device 102, a section for controlling howhearing-assistance device 102 attenuates wind noise, a second forfinding hearing-assistance device 102 if lost, and so on. Additionally,the GUI of companion application 424 may include a cognitive benefitsection that displays data regarding a cognitive benefit measure for thewearer of hearing-assistance device 102. In some examples, the cognitivebenefit section of companion application 424 displays a diagram similarto that shown in the example of FIG. 2 or the example of FIG. 8.Additionally, the GUI of companion application 424 may include a bodyfitness measure section that displays data regarding a body fitnessmeasure for the wearer of hearing-assistance device 102. In someexamples, the body fitness measure section of companion application 424displays a diagram similar to the example of FIG. 9, described below.The GUI of companion application 424 may also include a wellness measuresection that displays data regarding a wellness measure for the wearerof hearing-assistance device 102. In some examples, the wellness measuresection of companion application 424 displays a diagram similar to theexample of FIG. 10, described below.

In some examples, companion application 424 may request data forcalculating a cognitive benefit measure or body fitness measure fromhearing-assistance device 102 each time mobile device 106 receives anindication of user input to navigate to the cognitive benefit section orbody fitness measure section of companion application 424. In this way,a wearer of hearing-assistance device 102 may get real-time confirmationthat companion application 424 is communicating with hearing-assistancedevice 102, that the data displayed are current, and may ensure that thewireless transfer of the data-log data does not interrupt or interferewith other processes in companion application 424, or on computingdevice 400 device. Furthermore, requesting data from hearing-assistancedevice 102 only when computing device 400 receive an indication of userinput to navigate to the cognitive benefit section, the body fitnessmeasure section, or the wellness measure section of companionapplication 424 may reduce demands on a battery (e.g., battery 312 ofFIG. 3) of hearing-assistance device 102 (FIG. 1; FIG. 3), relative tocomputing device 400 requesting the data from hearing-assistance device102 on a periodic basis.

Companion application 424 may store one or more of various types of dataas historical data 426. Historical data 426 may comprise a database forstoring historic data related to cognitive benefit. For example,companion application 424 may store, in historical data 426, cognitivebenefit measures, body fitness measures, sub-component values, data fromhearing-assistance device 102, and/or other data. Companion application424 may retrieve data from historical data 426 to generate a GUI fordisplay of past cognitive benefit measures, body fitness measures, andwellness measures of the wearer of hearing-assistance device 102.

FIG. 5 is a flowchart illustrating an example operation of computingsystem 104, in accordance with one or more aspects of this disclosure.The flowcharts of this disclosure are provided as examples. In otherexamples, operations shown in the flowcharts may include more, fewer, ordifferent actions, or actions may be performed in different orders or inparallel.

In the example of FIG. 5, computing system 104 may receive data fromhearing-assistance device 102 (500). For example, computing system 104may receive data from hearing-assistance device 102 in response tocomputing system 104 sending a request for the data tohearing-assistance device 102. In some examples, computing system 104receives an indication of user input to access a cognitive benefitsection of a GUI of a software application (e.g., companion application424) running on computing system 104. In this example, in response toreceiving the indication of user input, computing system 104 sends arequest to hearing-assistance device 102 and computing system 104receives the data from hearing-assistance device 102 in response to therequest.

Additionally, computing system 104 may determine, based on the datareceived from hearing-assistance device 102, a cognitive benefit measurefor a wearer of hearing-assistance device 102 (502). The cognitivebenefit measure may be an indication of a change of a cognitive benefitof the wearer of hearing-assistance device 102 attributable to use ofhearing-assistance device 102 by the wearer of hearing-assistance device102. In some examples, computing system 104 may scale the cognitivebenefit measure based on an amount of time the wearer spends wearinghearing-assistance device 102.

Furthermore, in the example of FIG. 5, computing system 104 may outputan indication of the cognitive benefit measure (504). For example,computing system 104 may output a GUI for display that includes anumerical value indicating the cognitive benefit measure. In someexamples, to output the indication of the cognitive benefit measures,computing system 104 may send to hearing-assistance device 102 audiodata that represents the cognitive benefit measure in audible form.

As shown in the example of FIG. 5, computing system 104 may, as part ofdetermining the cognitive benefit measure for the wearer ofhearing-assistance device 102, determine a plurality of sub-componentsof the cognitive benefit measure (506). Additionally, computing system104 may determine the cognitive benefit measure based on the pluralityof sub-components of the cognitive benefit measure (508). For example,computing system 104 may determine a weighted average of the pluralityof sub-components to determine the cognitive benefit measure.

As part of determining the plurality of sub-components, computing system104 may determine an “audibility” sub-component, an “intelligibility”sub-component, a “comfort” sub-component, a “focus” sub-component, a“sociability” sub-component, and a “connectivity” sub-component. Forexample, computing system 104 may determine an audibility sub-componentthat is a measure of the improvement in audibility provided to thewearer by hearing-assistance device 102. For instance, the audibilitysub-component may indicate a measure of detected sounds that areamplified sounds. In this example, each of the detected sounds is asound detected by hearing-assistance device 102. Furthermore, in thisexample, each respective amplified sound is a sound that was amplifiedby hearing-assistance device 102 because the intensity of the sound wasbelow an audibility threshold of the wearer of hearing-assistance device102. In this example, the audibility threshold of the wearer ofhearing-assistance device 102 is an intensity level below which thewearer of hearing-assistance device 102 is unable to reliably hear thesound.

In some examples, computing system 104 may determine an intelligibilitysub-component that indicates a measure of an improvement in speechunderstanding provided by hearing-assistance device 102. Furthermore, insome examples, computing system 104 may determine a comfortsub-component that indicates a measure of noise reduction provided byhearing-assistance device 102. In some examples, computing system 104may determine a focus sub-component that indicates a measure of timehearing-assistance device 102 spends in directional processing modes. Inthis example, each of the respective directional processing modesselectively attenuates off-axis, unwanted sounds. Furthermore, in someexamples, computing system 104 may determine a sociability sub-componentthat indicates a measure of time spent in auditory environmentsinvolving speech. In some examples, computing system 104 may determine aconnectivity sub-component that indicates a measure of an amount of timehearing-assistance device 102 spent streaming media from devicesconnected wirelessly to hearing-assistance device 102.

In some examples, as part of determining the plurality ofsub-components, computing system 104 may determine a “use score”sub-component, an “engagement score” sub-component, and an “activelistening” sub-component. In such examples, computing system 104 maydetermine the cognitive benefit measure (e.g., a brain score) as aweighted sum of the “use score” sub-component, the “engagement score”sub-component, and the “active listening” sub-component. For instance,computing system 104 may determine the cognitive benefit measure suchthat a first percentage (e.g., 40%) of the cognitive benefit measure isbased on the “use score” sub-component, a second percentage (e.g., 40%)of the cognitive benefit measure is based on the “engagement score”sub-component, and a third percentage (e.g., 20%) of the cognitivebenefit measure is based on the “active listening” sub-component.

In such examples, the “use score” sub-component may be based on anamount of time during a scoring period that the wearer ofhearing-assistance device 102 has used hearing-assistance device 102.The wearer of hearing-assistance device 102 may be considered to beusing hearing-assistance device 102 when hearing-assistance device 102is in the wearer's ear and turned on. In some examples,hearing-assistance device 102 may determine whether hearing-assistancedevice 102 is in the wearer's ear based on one or more of varioussignals generated by sensors 310 (FIG. 3). For instance,hearing-assistance device 102 may determine whether hearing-assistancedevice 102 is in the wearer's ear based on a pattern of motion signalsgenerated by accelerometers 318 (FIG. 3), based on a body temperaturesignal from body temperature sensor 323, and/or based on a heart ratesignal from heart rate sensor 320. In some examples, computing system104 may determine the “use score” based on a comparison of an in-usetime to a time goal. The in-use time may indicate the amount of timethat hearing-assistance device 102 is in the wearer's ear and turned on.The time goal may be a predetermined amount of time (e.g., 12 hours).Thus, in one example, computing system 104 may determine the value ofthe “use score” sub-component based on the in-use time during thecurrent scoring period divided by the time goal, multiplied by a maximumvalue of the “use score” subcomponent. Thus, in this example, if thetime goal is 12 hours during the current scoring period, the maximumvalue of the “use score” subcomponent is 40 and the in-use time is 12hours, computing system 104 may determine that the value of the “usescore” sub-component is equal to 40. In some examples,hearing-assistance device 102 may record log data items in data log 324that include timestamps of when the wearer started and stopped wearinghearing-assistance device 102.

The “engagement score” sub-component may be a measure of how much thewearer of hearing-assistance device 102 participates in activitiesinvolving aural engagement during a scoring period. Example types ofactivities involving aural engagement include engaging in conversation,streaming audio data (e.g., streaming music, streaming audio data fromtelevision or a cinema), and other activities that involve the wearer ofhearing-assistance device 102 actively listening to sounds.

In examples where the value of the “engagement score” sub-component isbased on the wearer of hearing-assistance device 102 engaging inconversation, hearing-assistance device 102 may run an acousticclassifier that classifies sounds detected by hearing-assistance device102. For example, the acoustic classifier may determine whether thecurrent sound detected by hearing-assistance device 102 is silent,speaking and quiet, speaking with noise, music, and wind. In otherexamples, the acoustic classifier may classify the detected sounds intoother categories.

In some examples, computing system 104 may determine the value of the“engagement score” sub-component based at least in part on an amount oftime that the sound detected by hearing-assistance device 102 isclassified into a speech category. Hearing-assistance device 102 mayrecord transitions between categories as log data items in data log 324.In some examples, computing system 104 may determine the value of the“engagement score” sub-component based at least in part of a number oftimes that hearing-assistance device 102 determines during the currentscoring period that the type of sound detected by hearing-assistancedevice 102 transitions to a speech category from another type of sound.For instance, computing system 104 may determine the “engagement score”sub-component based on the progress of the wearer of hearing-assistancedevice 102 toward a goal of a particular amount of time that sounddetected by hearing-assistance device 102 is classified into a speechcategory

Furthermore, in some examples, computing system 104 may determine the“engagement score” sub-component based on multiple activities involvingaural engagement. For example, computing system 104 may determine afirst component of the “engagement score” sub-component based onengagement in conversation and a second component of the “engagementscore” sub-component based on streaming audio data. In some examples,hearing-assistance device 102 may record log data items in data log 324that include timestamps of when hearing-assistance device 102 startedand stopped streaming media data. In this example, the first factor maybe determined in the same manner as the “sociability” sub-componentdescribed elsewhere in this disclosure and the second factor may bedetermined in the same manner as the “connectivity” sub-componentdescribed elsewhere in this disclosure. In this example, a firstpercentage (e.g., 80%) of the “engagement score” sub-component may bebased on the first factor and a second percentage (e.g., 20%) of the“engagement score” sub-component may be based on the second factor. Forinstance, computing system 104 may determine the “engagement score” as aweighted sum of the first and second factors.

The “active listening” sub-component may be determined based on exposureof the wearer of hearing-assistance device 102 to a plurality ofdifferent acoustic environments during a current scoring period. Forexample, hearing-assistance device 102 may determine whether the sounddetected by hearing-assistance device 102 is associated with particulartypes of acoustic environments. Example types of acoustic environmentsmay include speech, speech with noise, quiet, machine noise, and music.In some examples, hearing-assistance device 102 may record log dataitems in data log 324 indicating transitions between acousticenvironments and timestamps associated with such transitions. Computingsystem 104 may increment, based on the log data, the “active listening”sub-component for each different type of acoustic environment thathearing-assistance device 102 detects during a scoring period. Forinstance, computing system 104 may increment the “active listening”sub-component by x₁ points (e.g., 4 points) for exposure to a firstacoustic environment, x₂ for exposure to a second acoustic environment,and so on, where x₁, x₂, . . . x₄ are the same value or two or moredifferent values. In some examples, computing system 104 may also oralternatively determine the value of the “active listening”sub-component based on progress of the wearer of hearing-assistancedevice 102 during the current scoring period toward a goal for the“active listening” sub-component. The goal for the “active listening”sub-component may be an amount of time that hearing-assistance device102 spends performing a specified function, such as processing speech,processing sound in a directional mode, etc. In another example, thegoal for the “active listening” sub-component may be a number ofacoustic environments that the wearer of hearing-assistance device 102is to experience during the scoring period.

Furthermore, in some examples, as shown in FIG. 5, computing system 104may store the cognitive benefit measure in a database (e.g., historicaldata 426 (FIG. 4)) of historical cognitive benefit measures for thewearer of hearing-assistance device 102 (510). Additionally, computingsystem 104 may output an indication of the historical cognitive benefitmeasures (512). For example, computing system 104 may output a graph fordisplay indicating cognitive benefit measures over time for the wearerof hearing-assistance device 102. In some examples, the historicalinformation may be based on an hour, day, week, month, and/or year, andmay be presented as discrete values or a trend (e.g., graph.). In someexamples, to output the indication of the historical cognitive benefitmeasures, computing system 104 may send to hearing-assistance device 102audio data that represents the historical cognitive benefit measures inaudible form.

FIG. 6 is a flowchart illustrating an example operation to compute abody fitness measure in accordance with one or more aspects of thisdisclosure. In the example of FIG. 6, computing system 104 may receivedata from hearing-assistance device 102 (600). For example, computingsystem 104 may receive data from hearing-assistance device 102 inresponse to computing system 104 sending a request for the data tohearing-assistance device 102. In some examples, computing system 104receives an indication of user input to access a body fitness measuresection of a graphical user interface (GUI) of a software application(e.g., companion application 424) running on computing system 104. Inthis example, in response to receiving the indication of user input,computing system 104 sends a request to hearing-assistance device 102and computing system 104 receives the data from hearing-assistancedevice 102 in response to the request. In some examples, computingsystem 104 may receive a copy of data in data log 324 (FIG. 3) inaccordance with any of the examples provided elsewhere in thisdisclosure.

Additionally, computing system 104 may determine, based on the datareceived from hearing-assistance device 102, a body fitness measure forthe wearer of hearing-assistance device 102 (602). The body fitnessmeasure may be an indication of a level of physical activity in whichthe wearer of hearing-assistance device 102 has engaged during a scoringperiod while wearing hearing-assistance device 102. In some examples,computing system 104 may scale the body fitness measure based on anamount of time the wearer of hearing-assistance device 102 spendswearing hearing-assistance device 102.

Furthermore, in the example of FIG. 6, computing system 104 may outputan indication of the body fitness measure (604). For example, computingsystem 104 may output a GUI for display that includes a numerical valueindicating the body fitness measure. FIG. 9, described in detail below,is an example GUI for display of the body fitness measure. In someexamples, to output the indication of the body fitness measure,computing system 104 may send to hearing-assistance device 102 audiodata that represents the body fitness measure in audible form.

As shown in the example of FIG. 6, computing system 104 may, as part ofdetermining the body fitness measure for the wearer ofhearing-assistance device 102, determine a plurality of sub-componentsof the body fitness measure (606). Additionally, computing system 104may determine the body fitness measure based on the plurality ofsub-components of the body fitness measure (608). For example, computingsystem 104 may determine a weighted average of the plurality ofsub-components to determine the body fitness measure.

As part of determining the plurality of sub-components, computing system104 may determine a “steps” sub-component, an “activity” sub-component,and a “move” sub-component. The “steps” sub-component may be based on anumber of steps (e.g., while walking or running) that the wearer ofhearing-assistance device 102 has taken during the current scoringperiod. In some examples, computing system 104 may determine a value ofthe “steps” sub-component based on the progress during the currentscoring period of the wearer of hearing-assistance device 102 toward agoal for the “steps” sub-component. Furthermore, in some examples, IMU326 determines the number of steps and hearing-assistance device 102writes data indicating the number of steps to data log 324. In someexamples, hearing-assistance device 102 stores timestamps with thenumber of steps.

The “activity” sub-component may be a measure of vigorous activity inwhich the wearer of hearing-assistance device 102 has engaged during thecurrent scoring period. For example, computing system 104 may incrementthe “activity” sub-component in response to determining that the wearerof hearing-assistance device 102 has performed a vigorous activity. Insome examples, computing system 104 may determine a value of the“activity” sub-component based on the progress during the currentscoring period of the wearer of hearing-assistance device 102 towardmeeting a goal for the “activity” sub-component. In such examples, thegoal for the “activity” sub-component may be defined as a number ofvigorous activities or amount of time engaged in vigorous activities tobe performed during the current scoring period.

Computing system 104 or hearing-assistance device 102 may determinewhether the wearer of hearing-assistance device 102 has performed avigorous activity in one or more of various ways. For example, computingsystem 104 or hearing-assistance device 102 may determine that thewearer of hearing-assistance device 102 has performed a vigorousactivity if computing system 104 has taken more than a given number ofsteps in a given amount of time. For instance, computing system 104 orhearing-assistance device 102 may assume that the wearer ofhearing-assistance device 102 has run (or engaged in an activity morevigorous than a brisk walk) if the wearer of hearing-assistance device102 has taken more than a threshold number of steps within a given timeperiod.

Hearing-assistance device 102 may store one or more of various types ofdata to data log 324 to enable computing system 104 to determine the“activity” sub-component. For example, IMU 326 may output the number ofsteps taken during a given period. For instance, for every minute, IMU326 may output the number of steps taken during that minute.Hearing-assistance device 102 may write a log data item including atimestamp to data log 324 if the number of steps taken during the givenperiod is greater than a threshold associated with vigorous activity.

The “move” sub-component may be based on a number time of intervalsduring the current scoring period in which the wearer ofhearing-assistance device 102 moves for a given amount of time. Forexample, computing system 104 may determine the “move” sub-component asa number of hours during a day in which the wearer of hearing-assistancedevice 102 was actively moving for more than 1 minute. In some examples,computing system 104 may determine the “move” sub-component based onprogress of the wearer of hearing-assistance device 102 during thecurrent scoring period toward a goal for the “move” sub-component. Insuch examples, the goal for the “move” sub-component may be defined as agiven number of time intervals during the current scoring period inwhich the wearer of hearing-assistance device 102 moves for the givenamount of time.

Hearing-assistance device 102 may store one or more of various types ofdata to data log 324 to enable computing system 104 to determine the“move” sub-component. For instance, in one example, hearing-assistancedevice 102 may receive timestamps from computing system 104 as describedelsewhere in this disclosure. Furthermore, in this example,hearing-assistance device 102 may write data to data log 324 indicatingthat the wearer has started moving with a first timestamp and dataindicating that the wearer has stopped moving with a second timestamp.Computing system 104 may analyze such data to determine whether thewearer of hearing-assistance device 102 was active for the given amountof time during a time interval.

Furthermore, in some examples, as shown in FIG. 6, computing system 104may store the body fitness measure in a database (e.g., historical data426 (FIG. 4)) of historical body fitness measures for the wearer ofhearing-assistance device 102 (610). Additionally, computing system 104may output an indication of the historical body fitness measures (612).For example, computing system 104 may output a graph for displayindicating body fitness measures over time for the wearer ofhearing-assistance device 102. In some examples, the historicalinformation may be based on an hour, day, week, month, or year, and maybe presented as discrete values or a trend (e.g., graph.). In someexamples, to output the indication of the historical body fitnessmeasures, computing system 104 may send to hearing-assistance device 102audio data that represents the historical body fitness measures inaudible form.

FIG. 7 is a flowchart illustrating an example operation to compute awellness measure in accordance with one or more aspects of thisdisclosure. In the example of FIG. 7, computing system 104 maydetermine, based on the data received from hearing-assistance device102, a cognitive benefit measure for a wearer of hearing-assistancedevice 102 (700). Computing system 104 may determine the cognitivebenefit measure in the manner described above with respect to FIG. 5.

Furthermore, in the example of FIG. 7, computing system 104 maydetermine, based on the data received from hearing-assistance device102, a body fitness measure for the wearer of hearing-assistance device102 (702). Computing system 104 may determine the body fitness measurein the manner described above with respect to FIG. 6.

Computing system 104 may determine a wellness measure based on thecognitive benefit measure for the wearer of hearing-assistance device102 and the body fitness measure for the wearer of hearing-assistancedevice 102 (704). In various examples, computing system 104 maydetermine the wellness measure in various ways. For example, computingsystem 104 may determine the wellness measure as a weighted sum of thecognitive benefit measure and the body fitness measure. For instance, inthis example, computing system 104 may determine the wellness measurewith equal weightings, e.g., a 50% weighting to the cognitive benefitmeasure and 50% weighting to the body fitness measure. In otherexamples, computing system 104 may use unbalanced (i.e., different)weightings of the cognitive benefit measure and the body fitnessmeasure. The weighting for the cognitive benefit measure may be greaterthan the weighting for the body fitness measure. Alternatively, theweighting for the cognitive benefit measure may be less than theweighting for the body fitness measure. As one example, computing system104 may determine the wellness measure with a 60% weighting to thecognitive benefit measure and a 40% weighting to the body fitnessmeasure.

In the example of FIG. 7, computing system 104 may output an indicationof the wellness measure (706). For example, computing system 104 mayoutput a GUI for display that includes a numerical value indicating thewellness measure. FIG. 10, described in detail below, is an example GUIfor display of the wellness measure. In some examples, to output theindication of the wellness measure, computing system 104 may send tohearing-assistance device 102 audio data that represents the wellnessmeasure in audible form.

Furthermore, in some examples, as shown in FIG. 7, computing system 104may store the wellness measure in a database (e.g., historical data 426(FIG. 4)) of historical wellness measures for the wearer ofhearing-assistance device 102 (708). Additionally, computing system 104may output an indication of the historical wellness measures (710). Forexample, computing system 104 may output a graph for display indicatingwellness measures over time for the wearer of hearing-assistance device102. In some examples, the historical information may be based on anhour, day, week, month, or year, and may be presented as discrete valuesor a trend (e.g., graph.). In some examples, to output the indication ofthe historical wellness measures, computing system 104 may send tohearing-assistance device 102 audio data that represents the historicalwellness measures in audible form.

FIG. 8 is an example GUI 800 for display of a cognitive benefit measurein accordance with one or more aspects of this disclosure. In theexample of FIG. 8, GUI 800 includes controls 802 that allow a user toswitch between a user interface for display of the cognitive benefitmeasure and a user interface for display of a body fitness measure.

In the example of FIG. 8, the cognitive benefit measure is based on ause score sub-component, an engagement score sub-component, and anactive listening sub-component. Computing system 104 may determine theuse score sub-component, the engagement score sub-component, and theactive listening sub-component in the manner described in any of theexamples provided elsewhere in this disclosure. Feature 804 of GUI 800indicates a value of the use score sub-component. Feature 806 of GUI 800indicates a value of the engagement score sub-component. Feature 808 ofGUI 800 indicates a value of the active listening sub-component. Infeatures 804, 806, and 808 of GUI 800, the value before the “/” markindicates a current value of the sub-component and the value after the“/” mark indicates a goal for the sub-component.

Furthermore, in the example of FIG. 8, GUI 800 includes a circulardiagram 810 having segments corresponding to the sub-components of thecognitive benefit measure. Each of the segments is filled in an amountproportional to the wearer's progress toward meeting the goals for thesub-components of the cognitive benefit measure. Additionally, circulardiagram 810 may include a numerical value indicating the wearer'scognitive benefit measure (e.g., 57 in the example of FIG. 8) and anumerical value indicating the wearer's cognitive benefit measure goalfor the cognitive benefit measure (e.g., 100 in the example of FIG. 8).The wearer's cognitive benefit measure goal is the wearer's goal for thecognitive benefit measure.

GUI 800 also includes historical icons 812A, 812B, and 812C(collectively, “historical icons 812”). In the example of FIG. 8, likecircular diagram 810, historical icons 812 include segments with filledportions corresponding to indicate the wearer's progress toward meetingthe goals for the sub-components on previous days, e.g., Saturday,Sunday and Monday in the example of FIG. 8. In response to receiving anindication of user selection of one of historical icons 812, computingsystem 104 may output for display a GUI having more details regardingthe wearer's cognitive benefit measure for the day corresponding to theselected historical icon.

FIG. 9 is an example GUI 900 for display of a body fitness measure inaccordance with one or more aspects of this disclosure. In the exampleof FIG. 9, GUI 900 includes controls 902 that allow a user to switchbetween a user interface for display of the cognitive benefit measureand a user interface for display of a body fitness measure.

In the example of FIG. 9, the body fitness measure is based on a stepssub-component, an activity sub-component, and a movement sub-component.Computing system 104 may determine the steps sub-component, the activitysub-component, and the movement sub-component in the manner described inany of the examples provided elsewhere in this disclosure. Feature 904of GUI 900 indicates a value of the steps sub-component. Feature 906 ofGUI 900 indicates a value of the activity sub-component. Feature 908 ofGUI 900 indicates a value of the movement sub-component. In features904, 906, and 908 of GUI 900, the value before the “/” mark indicates acurrent value of the sub-component and the value after the “/” markindicates a goal for the sub-component.

Furthermore, in the example of FIG. 9, GUI 900 includes a circulardiagram 910 having segments corresponding to the sub-components of thebody fitness measure. Each of the segments is filled in an amountproportional to the wearer's progress toward meeting the goals for thesub-components of the body fitness measure. Additionally, circulardiagram 910 may include a numerical value indicating the wearer's bodyfitness measure (e.g., 100 in the example of FIG. 9) and a numericalvalue indicating the wearer's body fitness measure goal (e.g., 100 inthe example of FIG. 9).

GUI 900 also includes historical icons 912A, 912B, and 912C(collectively, “historical icons 912”). Like circular diagram 910,historical icons 912 include segments with filled portions correspondingto the wearer's progress toward meeting the goals for the sub-componentson previous days, e.g., Saturday, Sunday and Monday in the example ofFIG. 9. In response to receiving an indication of user selection of oneof historical icons 912, computing system 104 may output for display aGUI having more details regarding the wearer's cognitive benefit measurefor the day corresponding to the selected historical circular diagram.

FIG. 10 is an example GUI 1000 for display of a wellness measure inaccordance with one or more aspects of this disclosure. In the exampleof FIG. 10, GUI 1000 includes a body fitness measure feature 1002 and acognitive benefit measure feature 1004. Body fitness measure feature1002 is filled in an amount proportional to the wearer's progress towardmeeting the wearer's body fitness measure goal. Cognitive benefitmeasure feature 1004 is filled in an amount proportional to the wearer'sprogress toward meeting the wearer's cognitive benefit measure goal. Thewearer's cognitive benefit measure goal may also be referred to hereinas the cognitive benefit measure goal. Additionally, in the example ofFIG. 10, GUI 1000 includes a wellness measure feature 1006 (e.g.,indicated by “Thrive Score” in FIG. 10) that includes a numeric valueindicating the wearer's wellness measure.

In the example of FIG. 10, GUI 100 may be a primary screen of companionapplication 424. Because controlling the volume of hearing-assistancedevice(s) may be the feature for which the user uses companionapplication 424 the most, GUI 1000 may be designed to indicate thewearer's wellness measure along with volume controls 1008 in order tobring the wearer's wellness measure to the user's attention.

In some examples, a processing system (e.g., in computing system 104,hearing-assistance device 102, or another device) may detect one or moreuser behavior conditions using hearing-assistance device 102. Theprocessing system may comprise one or more processors. The user behaviorconditions may be measures of behavior of the wearer ofhearing-assistance device 102. In this example, the processing systemmay determine a wellness measure based on the one or more conditions.

The user behavior conditions may include the cognitive benefit measure,the body fitness measure, or other measures of the behavior of thewearer of hearing-assistance device 102. For instance, the cognitivebenefit measure may be considered a measure of user behavior withrespect to how the wearer of hearing-assistance device 102 useshearing-assistance device 102. Similarly, the body fitness measure maybe considered a measure of user behavior with respect to physicalactivity behavior in which the wearer of hearing-assistance device 102engages. Thus, detecting one or more user behavior conditions mayinclude detecting activity information (e.g., the body fitness measure)and detecting hearing information (e.g., the cognitive benefit measure).For instance, the processing system may determine a cognitive measureand a body measure. The processing system may further determine thewellness measure using the cognitive measure and the body measure. Insome such examples, hearing-assistance device 102 determines thewellness measure.

In some examples, the processing system may determine the wellnessmeasure based at least in part on the activity information and thehearing information. In some examples, the hearing information includesone or more of hearing aid usage, user engagement, and active listening.In some examples, information relating to the user behavior conditionsmay be wirelessly transmitted from the hearing-assistance device to acomputing device (e.g., a computing device in computing system 104) andthe computing device determines the wellness measure using thetransmitted information.

The following paragraphs provide examples in accordance with techniquesof this disclosure.

Example 1. A method comprising: receiving, by a computing systemcomprising one or more electronic computing devices, data from ahearing-assistance device; determining, by a computing system, based onthe data received from the hearing-assistance device, a cognitivebenefit measure for a wearer of the hearing-assistance device, thecognitive benefit measure being an indication of a change of a cognitivebenefit of the wearer of the hearing-assistance device attributable touse of the hearing-assistance device by the wearer of thehearing-assistance device; and outputting, by the computing system, anindication of the cognitive benefit measure.

Example 2. The method of example 1, wherein determining the cognitivebenefit measure comprises: determining, by the computing system, aplurality of sub-components of the cognitive benefit measure; anddetermining, by the computing system, the cognitive benefit measurebased on the plurality of sub-components of the cognitive benefitmeasure.

Example 3. The method of example 2, wherein determining the plurality ofsub-components comprises one or more of: determining, by the computingsystem, an audibility sub-component is a measure of an improvement inaudibility provided to the wearer by the hearing-assistance device,determining, by the computing system, an intelligibility sub-componentthat indicates a measure of an improvement in speech understandingprovided by the hearing-assistance device, determining, by the computingsystem, a comfort sub-component that indicates a measure of noisereduction provided by the hearing-assistance device, determining, by thecomputing system, a focus sub-component that indicates a measure of timethe hearing-assistance device spends in directional processing modes,each of the respective directional processing modes selectivelyattenuating off-axis, unwanted sounds, determining, by the computingsystem, a sociability sub-component that indicates a measure of timespent in auditory environments involving speech, or determining, by thecomputing system, a connectivity sub-component that indicates a measureof an amount of time the hearing-assistance device spent streaming mediafrom devices connected wirelessly to the hearing-assistance device.

Example 4. The method of any of examples 2 or 3, wherein determining thecognitive benefit measure comprises determining, by the computingsystem, a weighted average of the plurality of sub-components.

Example 5. The method of any of examples 2 or 4, wherein determining theplurality of sub-components comprises one or more of: determining, bythe computing system, a use score sub-component that is based on howmuch time the wearer of the hearing-assistance device uses thehearing-assistance device during a scoring period, determining, by thecomputing system, an engagement score sub-component that is a measure ofhow much the wearer of the hearing-assistance device participates inactivities involving aural engagement during the scoring period, ordetermining, by the computing system, an active listening sub-componentbased on exposure of the wearer of the hearing-assistance device to aplurality of different acoustic environments during the scoring period.

Example 6. The method of any of examples 1-5, further comprising:receiving, by the computing system, an indication of user input toaccess a cognitive benefit section of a graphical user interface (GUI)of a software application running on the computing system; and inresponse to receiving the indication of user input, sending, by thecomputing system, a request to the hearing-assistance device, whereinthe computing system receives the data from the hearing-assistancedevice in response to the request.

Example 7. The method of example 6, wherein determining the cognitivebenefit measure comprises scaling, by the computing system, thecognitive benefit measure based on an amount of time the wearer spendswearing the hearing-assistance device.

Example 8. The method of any of examples 1-7, further comprising:storing, by the computing system, the cognitive benefit measure in adatabase of historical cognitive benefit measures for the wearer; andoutputting, by the computing system, an indication of the historicalcognitive benefit measures.

Example 9. The method of any of examples 1-8, further comprising:determining, by the computing system, based on the data received fromthe hearing-assistance device, a body measure for the wearer of thehearing-assistance device, the body measure being an indication ofphysical activity in which the wearer of the hearing-assistance deviceengages while wearing the hearing-assistance device; and outputting, bythe computing system, an indication of the body measure.

Example 10. The method of example 9, further comprising: determining, bythe computing system, based on the cognitive benefit measure and thebody measure, a wellness measure for the wearer of thehearing-assistance device, the wellness measure for the wearer of thehearing-assistance device being an indication of an overall wellness ofthe wearer of the hearing-assistance device; and outputting, by thecomputing system, an indication of the wellness measure.

Example 11. The method of any of examples 1-10, wherein: the methodfurther comprises sending, by the computing system, timestamps to thehearing-assistance device, receiving the data from thehearing-assistance device comprises receiving, by the computing system,a plurality of log data items, each of the log data items comprising logdata and one of the timestamps, and determining the cognitive benefitmeasure comprises determining, by the computing system, the cognitivebenefit measure based on the timestamps and the log data in the log dataitems.

Example 12. The method of any of examples 1-11, further comprising:sending, by the computing system, based on the cognitive benefitmeasure, an alert to the wearer of the hearing-assistance device oranother person.

Example 13. A computing system comprising: a radio configured to receivedata from a hearing-assistance device; and one or more processorsconfigured to: determine, based on the data received from thehearing-assistance device, a cognitive benefit measure for a wearer ofthe hearing-assistance device, the cognitive benefit measure being anindication of a change of a cognitive benefit of the wearer of thehearing-assistance device attributable to use of the hearing-assistancedevice by the wearer of the hearing-assistance device; and output anindication of the cognitive benefit measure.

Example 14. The computing system of example 13, wherein the one or moreprocessors are configured to: determine a plurality of sub-components ofthe cognitive benefit measure; and determine the cognitive benefitmeasure based on the plurality of sub-components of the cognitivebenefit measure.

Example 15. The computing system of example 14, wherein the one or moreprocessors are configured such that, as part of determining theplurality of sub-components, the one or more processors determine one ormore of: an audibility sub-component that is a measure of an improvementin audibility provided to the wearer by the hearing-assistance device,an intelligibility sub-component that indicates a measure of animprovement in speech understanding provided by the hearing-assistancedevice, a comfort sub-component that indicates a measure of noisereduction provided by the hearing-assistance device, a focussub-component that indicates a measure of time the hearing-assistancedevice spends in directional processing modes, each of the respectivedirectional processing modes selectively attenuating off-axis, unwantedsounds, a sociability sub-component that indicates a measure of timespent in auditory environments involving speech, or a connectivitysub-component that indicates a measure of an amount of time thehearing-assistance device spent streaming media from devices connectedwirelessly to the hearing-assistance device.

Example 16. The computing system of any of examples 14 or 15, whereinthe one or more processors are configured such that, as part ofdetermining the cognitive benefit measure, the one or more processorsdetermine a weighted average of the plurality of sub-components.

Example 17. The computing system of any of examples 14 or 16, whereinthe one or more processors are configured such that, as part ofdetermining the plurality of sub-components, the one or more processorsdetermine one or more of: a use score sub-component that is based on howmuch time the wearer of the hearing-assistance device uses thehearing-assistance device during a scoring period, an engagement scoresub-component that is a measure of how much the wearer ofhearing-assistance device participates in activities involving auralengagement during the scoring period, or an active listeningsub-component based on exposure of the wearer of the hearing-assistancedevice to a plurality of different acoustic environments during thescoring period.

Example 18. The computing system of any of examples 13-17, wherein theone or more processors are further configured to: receive an indicationof user input to access a cognitive benefit section of a graphical userinterface (GUI) of a software application running on the computingsystem; and in response to receiving the indication of user input, senda request to the hearing-assistance device, wherein the computing systemreceives the data from the hearing-assistance device in response to therequest.

Example 19. The computing system of any of examples 13-18, wherein theone or more processors are configured such that, as part of determiningthe cognitive benefit measure, the one or more processors scale thecognitive benefit measure based on an amount of time the wearer spendswearing the hearing-assistance device.

Example 20. The computing system of any of examples 13-19, wherein theone or more processors are further configured to: store the cognitivebenefit measure in a database of historical cognitive benefit measuresfor the wearer; and output an indication of the historical cognitivebenefit measures.

Example 21. The computing system of any of examples 13-20, wherein theone or more processors are further configured to: determine, based onthe data received from the hearing-assistance device, a body measure forthe wearer of the hearing-assistance device, the body measure being anindication of physical activity in which the wearer of thehearing-assistance device engages while wearing the hearing-assistancedevice; and output an indication of the body measure.

Example 22. The computing system of example 21, wherein the one or moreprocessors are further configured to: determine, based on the cognitivebenefit measure and the body measure, a wellness measure for the wearerof the hearing-assistance device, the wellness measure for the wearer ofthe hearing-assistance device being an indication of an overall wellnessof the wearer of the hearing-assistance device; and output an indicationof the wellness measure.

Example 23. The computing system of any of examples 13-22, wherein: theone or more processors are configured to cause the computing system tosend timestamps to the hearing-assistance device, the computing systemis configured to receive a plurality of log data items from thehearing-assistance device, each of the log data items comprising logdata and one of the timestamps, and the one or more processors areconfigured to determine the cognitive benefit measure based on thetimestamps and the log data in the log data items.

Example 24. The computing system of any of examples 13-24, wherein theone or more processors are further configured to: send, based on thecognitive benefit measure, an alert to the wearer of thehearing-assistance device or another person.

Example 25. A non-transitory computer-readable storage medium havinginstructions stored thereon that, when executed, cause a computingsystem to: receive data from a hearing-assistance device; determine,based on the data received from the hearing-assistance device, acognitive benefit measure for a wearer of the hearing-assistance device,the cognitive benefit measure being an indication of a change of acognitive benefit of the wearer of the hearing-assistance deviceattributable to use of the hearing-assistance device by the wearer ofthe hearing-assistance device; and output an indication of the cognitivebenefit measure.

Example 26. The non-transitory computer-readable storage medium havinginstructions stored thereon that, when executed, cause the one or moreprocessors to perform the methods of any of examples 13-24.

Example 27. A computing system comprising: means for receiving data froma hearing-assistance device; and means for determining, based on thedata received from the hearing-assistance device, a cognitive benefitmeasure for a wearer of the hearing-assistance device, the cognitivebenefit measure being an indication of a change of a cognitive benefitof the wearer of the hearing-assistance device attributable to use ofthe hearing-assistance device by the wearer of the hearing-assistancedevice; and means for outputting an indication of the cognitive benefitmeasure.

Example 28. The computing system of example 27, the means fordetermining the cognitive benefit measure comprises: means fordetermining a plurality of sub-components of the cognitive benefitmeasure; and means for determining the cognitive benefit measure basedon the plurality of sub-components of the cognitive benefit measure.

Example 29. The computing system of example 28, the means fordetermining the plurality of sub-components comprises means fordetermining one or more of:

an audibility sub-component that is a measure of an improvement inaudibility provided to the wearer by the hearing-assistance device,

an intelligibility sub-component that indicates a measure of animprovement in speech understanding provided by the hearing-assistancedevice,

a comfort sub-component that indicates a measure of noise reductionprovided by the hearing-assistance device,

a focus sub-component that indicates a measure of time thehearing-assistance device spends in directional processing modes, eachof the respective directional processing modes selectively attenuatingoff-axis, unwanted sounds,

a sociability sub-component that indicates a measure of time spent inauditory environments involving speech, or

a connectivity sub-component that indicates a measure of an amount oftime the hearing-assistance device spent streaming media from devicesconnected wirelessly to the hearing-assistance device.

Example 30. The computing system of any of examples 28 or 29, whereinthe means for determining the cognitive benefit measure comprises meansfor determining a weighted average of the plurality of sub-components.

Example 31. The computing system of any of examples 28 or 30, whereinthe means for determining the plurality of sub-components comprisesmeans for determining one or more of:

a use score sub-component that is based on how much time the wearer ofthe hearing-assistance device uses the hearing-assistance device duringa scoring period,

an engagement score sub-component that is a measure of how much thewearer of hearing-assistance device participates in activities involvingaural engagement during the scoring period, or

an active listening sub-component based on exposure of the wearer of thehearing-assistance device to a plurality of different acousticenvironments during the scoring period.

Example 31. The computing system of any of examples 27-30, furthercomprising: means for receiving an indication of user input to access acognitive benefit section of a graphical user interface (GUI) of asoftware application running on the computing system; and means forsending, in response to receiving the indication of user input, arequest to the hearing-assistance device, wherein the computing systemreceives the data from the hearing-assistance device in response to therequest.

Example 32. The computing system of any of examples 27-31, wherein themeans for determining the cognitive benefit measure comprises means forscaling the cognitive benefit measure based on an amount of time thewearer spends wearing the hearing-assistance device.

Example 33. The computing system of any of examples 27-32, furthercomprising: means for storing the cognitive benefit measure in adatabase of historical cognitive benefit measures for the wearer; andmeans for outputting an indication of the historical cognitive benefitmeasures.

Example 34. The computing system of any of examples 27-33, furthercomprising: means for determining, based on the data received from thehearing-assistance device, a body measure for the wearer of thehearing-assistance device, the body measure being an indication ofphysical activity in which the wearer of the hearing-assistance deviceengages while wearing the hearing-assistance device; and means foroutputting an indication of the body measure.

Example 35. The computing system of example 34, further comprising:means for determining, based on the cognitive benefit measure and thebody measure, a wellness measure for the wearer of thehearing-assistance device, the wellness measure for the wearer of thehearing-assistance device being an indication of an overall wellness ofthe wearer of the hearing-assistance device; and means for outputting anindication of the wellness measure.

Example 36. The computing system of any of examples 27-35, furthercomprising: means for causing the computing system to send timestamps tothe hearing-assistance device; means for receiving a plurality of logdata items from the hearing-assistance device, each of the log dataitems comprising log data and one of the timestamps, and means fordetermining the cognitive benefit measure based on the timestamps andthe log data in the log data items.

Example 37. The computing system of any of examples 27-36, furthercomprising means for sending, based on the cognitive benefit measure, analert to the wearer of the hearing-assistance device or another person.

Example 38. A method comprising: detecting one or more user behaviorconditions using a hearing-assistance device; and determining a wellnessmeasure based on the one or more conditions.

Example 39. The method of example 38, wherein detecting one or more userbehavior conditions includes detecting activity information anddetecting hearing information, and the wellness measure is based atleast in part on the activity information and the hearing information.

Example 40. The method of example 39, wherein the hearing informationincludes one or more of hearing aid usage, user engagement, and activelistening.

Example 41. The method of examples 38 or 39, further comprisingwirelessly transmitting information relating to the user behaviorconditions from the hearing-assistance device to a computing device, andthe computing device determines the wellness measure using thetransmitted information.

Example 42. The method of any one or any combination of examples 38-41,comprising determining a cognitive measure and a body measure, anddetermining the wellness measure using the cognitive measure and thebody measure.

Example 43. The method of any one or any combination of examples 38-42,wherein the hearing-assistance device determines the wellness measure.

It is to be recognized that depending on the example, certain acts orevents of any of the techniques described herein can be performed in adifferent sequence, may be added, merged, or left out altogether (e.g.,not all described acts or events are necessary for the practice of thetechniques). Moreover, in certain examples, acts or events may beperformed concurrently, e.g., through multi-threaded processing,interrupt processing, or multiple processors, rather than sequentially.

In one or more examples, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over, as oneor more instructions or code, a computer-readable medium and executed bya hardware-based processing unit. Computer-readable media may includecomputer-readable storage media, which corresponds to a tangible mediumsuch as data storage media, or communication media including any mediumthat facilitates transfer of a computer program from one place toanother, e.g., according to a communication protocol. In this manner,computer-readable media generally may correspond to (1) tangiblecomputer-readable storage media which is non-transitory or (2) acommunication medium such as a signal or carrier wave. Data storagemedia may be any available media that can be accessed by one or morecomputers or one or more processing circuits to retrieve instructions,code and/or data structures for implementation of the techniquesdescribed in this disclosure. A computer program product may include acomputer-readable medium.

By way of example, and not limitation, such computer-readable storagemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage, or other magnetic storage devices, flashmemory, cache memory, or any other medium that can be used to storedesired program code in the form of instructions or data structures andthat can be accessed by a computer. Also, any connection is properlytermed a computer-readable medium. For example, if instructions aretransmitted from a website, server, or other remote source using acoaxial cable, fiber optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infrared, radio, and microwave,then the coaxial cable, fiber optic cable, twisted pair, DSL, orwireless technologies such as infrared, radio, and microwave areincluded in the definition of medium. It should be understood, however,that computer-readable storage media and data storage media do notinclude connections, carrier waves, signals, or other transient media,but are instead directed to non-transient, tangible storage media. Diskand disc, as used herein, includes compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk and Blu-raydisc, where disks usually reproduce data magnetically, while discsreproduce data optically with lasers. Combinations of the above shouldalso be included within the scope of computer-readable media.

Functionality described in this disclosure may be performed by fixedfunction and/or programmable processing circuitry. For instance,instructions may be executed by fixed function and/or programmableprocessing circuitry. Such processing circuitry may include one or moreprocessors, such as one or more digital signal processors (DSPs),general purpose microprocessors, application specific integratedcircuits (ASICs), field programmable logic arrays (FPGAs), or otherequivalent integrated or discrete logic circuitry. Accordingly, the term“processor,” as used herein may refer to any of the foregoing structureor any other structure suitable for implementation of the techniquesdescribed herein. In addition, in some aspects, the functionalitydescribed herein may be provided within dedicated hardware and/orsoftware modules configured for encoding and decoding, or incorporatedin a combined codec. Also, the techniques could be fully implemented inone or more circuits or logic elements. Processing circuits may becoupled to other components in various ways. For example, a processingcircuit may be coupled to other components via an internal deviceinterconnect, a wired or wireless network connection, or anothercommunication medium.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including a wireless handset, an integratedcircuit (IC) or a set of ICs (e.g., a chip set). Various components,modules, or units are described in this disclosure to emphasizefunctional aspects of devices configured to perform the disclosedtechniques, but do not necessarily require realization by differenthardware units. Rather, as described above, various units may becombined in a hardware unit or provided by a collection ofinteroperative hardware units, including one or more processors asdescribed above, in conjunction with suitable software and/or firmware.

Various examples have been described. These and other examples arewithin the scope of the following claims.

1-20. (canceled)
 21. A method comprising: receiving, by a computingsystem comprising one or more electronic computing devices, data from ahearing-assistance device; determining, by the computing system, basedon the data received from the hearing-assistance device, an amount oftime that a wearer of the hearing-assistance device spends inconversation; determining, by the computing system, based on the amountof time that the wearer of the hearing-assistance device spends inconversation, a wellness measure for the wearer of thehearing-assistance device; and outputting, by the computing system, anindication of the wellness measure.
 22. The method of claim 21, wherein:the method further comprises determining, by the computing system, basedon the data received from the hearing-assistance device, a body measurefor the wearer of the hearing-assistance device, the body measure beingan indication of physical activity in which the wearer of thehearing-assistance device engaged while wearing the hearing-assistancedevice; and determining the wellness measure comprises determining, bythe computing system, the wellness measure based on the body measure andthe amount of time that the wearer of the hearing-assistance devicespends in conversation.
 23. The method of claim 21, wherein: the methodfurther comprises determining, by the computing system, a plurality ofsub-components, the plurality of sub-components including the amount oftime that the wearer of the hearing-assistance device spends inconversation and one or more of: an audibility sub-component thatindicates a measure of an improvement in audibility provided to thewearer by the hearing-assistance device, an intelligibilitysub-component that indicates a measure of an improvement in speechunderstanding provided by the hearing-assistance device, a comfortsub-component that indicates a measure of noise reduction provided bythe hearing-assistance device, a focus sub-component that indicates ameasure of time the hearing-assistance device spends in directionalprocessing modes, each of the respective directional processing modesselectively attenuating off-axis, unwanted sounds, or a connectivitysub-component that indicates a measure of an amount of time thehearing-assistance device spent streaming media from devices connectedwirelessly to the hearing-assistance device, and determining thewellness measure comprises determining, by the computing system, thewellness measure based on one or more of the plurality ofsub-components.
 24. The method of claim 23, further comprisingoutputting, by the computing system, a user interface that indicatesvalues for the plurality of sub-components.
 25. The method of claim 23,wherein: the method further comprises determining a cognitive benefitmeasure based on the plurality of sub-components, wherein the cognitivebenefit measure is an indication of a cognitive benefit of the wearer ofthe hearing-assistance device attributable to use of thehearing-assistance device by the wearer of the hearing-assistancedevice, determining the wellness measure comprises determining, by thecomputing system, the wellness measure based on the cognitive benefitmeasure.
 26. The method of claim 21, further comprising: receiving, bythe computing system, an indication of user input to access a cognitivebenefit section of a graphical user interface (GUI) of a softwareapplication running on the computing system; and in response toreceiving the indication of user input, sending, by the computingsystem, a request to the hearing-assistance device, wherein thecomputing system receives the data from the hearing-assistance device inresponse to the request.
 27. The method of claim 21, wherein thewellness measure for the wearer of the hearing-assistance device is anindication of an overall wellness of the wearer of thehearing-assistance device.
 28. A computing system comprising: acommunication unit configured to receive data from a hearing-assistancedevice; and one or more processors implemented in circuitry, the one ormore processors configured to: determine, based on the data receivedfrom the hearing-assistance device, an amount of time that a wearer ofthe hearing-assistance device spends in conversation; determine, basedon the amount of time that the wearer of the hearing-assistance devicespends in conversation, a wellness measure for the wearer of thehearing-assistance device; and output an indication of the wellnessmeasure
 29. The computing system of claim 28, wherein: the one or moreprocessors are further configured to determine, based on the datareceived from the hearing-assistance device, a body measure for thewearer of the hearing-assistance device, the body measure being anindication of physical activity in which the wearer of thehearing-assistance device engaged while wearing the hearing-assistancedevice; and the one or more processors are configured to determine thewellness measure based on the body measure and the amount of time thatthe wearer of the hearing-assistance device spends in conversation. 30.The computing system of claim 28, wherein the one or more processors areconfigured to determine a plurality of sub-components, the plurality ofsub-components including the amount of time that the wearer of thehearing-assistance device spends in conversation and one or more of: anaudibility sub-component that indicates a measure of an improvement inaudibility provided to the wearer by the hearing-assistance device, anintelligibility sub-component that indicates a measure of an improvementin speech understanding provided by the hearing-assistance device, acomfort sub-component that indicates a measure of noise reductionprovided by the hearing-assistance device, a focus sub-component thatindicates a measure of time the hearing-assistance device spends indirectional processing modes, each of the respective directionalprocessing modes selectively attenuating off-axis, unwanted sounds, or aconnectivity sub-component that indicates a measure of an amount of timethe hearing-assistance device spent streaming media from devicesconnected wirelessly to the hearing-assistance device, and the one ormore processors are configured to determine the wellness measure basedon one or more of the plurality of sub-components.
 31. The computingsystem of claim 30, wherein the one or more processors are furtherconfigured to output a user interface that indicates values for theplurality of sub-components.
 32. The computing system of claim 30, theone or more processors are configured to determine a cognitive benefitmeasure based on the plurality of sub-components, wherein the cognitivebenefit measure is an indication of a cognitive benefit of the wearer ofthe hearing-assistance device attributable to use of thehearing-assistance device by the wearer of the hearing-assistancedevice, and the one or more processors are configured to determine thewellness measure based on the cognitive benefit measure.
 33. Thecomputing system of claim 28, wherein the one or more processors arefurther configured to: receive an indication of user input to access acognitive benefit section of a graphical user interface (GUI) of asoftware application running on the computing system; and in response toreceiving the indication of user input, send a request to thehearing-assistance device, wherein the computing system receives thedata from the hearing-assistance device in response to the request. 34.The computing system of claim 28, wherein the wellness measure for thewearer of the hearing-assistance device is an indication of an overallwellness of the wearer of the hearing-assistance device.
 35. Anon-transitory computer-readable storage medium having instructionsstored thereon that, when executed, cause a computing system to: receivedata from a hearing-assistance device; determine, based on the datareceived from the hearing-assistance device, an amount of time that awearer of the hearing-assistance device spends in conversation;determine, based on the amount of time that the wearer of thehearing-assistance device spends in conversation, a wellness measure forthe wearer of the hearing-assistance device; and output an indication ofthe wellness measure.
 36. The non-transitory computer-readable storagemedium of claim 35, wherein: execution of the instructions furthercauses the computing system to determine, based on the data receivedfrom the hearing-assistance device, a body measure for the wearer of thehearing-assistance device, the body measure being an indication ofphysical activity in which the wearer of the hearing-assistance deviceengaged while wearing the hearing-assistance device; and execution ofthe instructions causes the computing system to determine the wellnessmeasure based on the body measure and the amount of time that the wearerof the hearing-assistance device spends in conversation.